Cascade recognition for personal tracking via unmanned aerial vehicle (UAV)

ABSTRACT

Systems and methods for tracking a subject using an unmanned aerial vehicle (UAV) are disclosed. An unmanned aerial vehicle (UAV) includes an onboard camera to capture/stream multiple images. An onboard visual recognition module isolates image elements by analyzing the captured images, and determines whether the image elements correspond or do not correspond to the subject. Current image elements corresponding to a subject are stored in a positive database, and non-corresponding current image elements are stored in a negative database. An onboard subject tracking module defines the chosen subject, determines attributes of the subject based on comparisons of image elements, and follows the subject by directing the attitude control system to adjust the velocity and orientation of the UAV based on the determined attributes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to provisionalpatent application U.S. Ser. No. 61/949,801 filed on Mar. 7, 2014. Saidapplication is herein incorporated by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present invention relate generally to unmanned aerialvehicles (UAVs) and particularly to systems and methods for selectingand following a moving subject individual or object while photographingthe subject via onboard camera.

BACKGROUND

Live broadcasting of competitions or athletic events in real time ornear real time is generally accomplished with large camera crews andmultiple camera operators, each having their own video/audio equipment.Stationary mounts, helicopters, blimps, and other bulky vehicles havealso been used. The space and time necessary to coordinate the largecrews and bulky vehicles can be economically and logistically taxing.Additionally, for individuals or small, informal groups interested incapturing video and/or images from inaccessible locations and streamingthese images to one or more remote viewers in real time or near realtime, professional-grade live broadcasting equipment of this nature isnot feasible. Therefore, a smaller, less logistically demanding, andeconomically feasible alternative for live broadcasting is desirable.

SUMMARY

It is to be understood that both the foregoing general description andthe following detailed description are only for illustrative andexplanatory purposes, and are not necessarily restrictive of theinvention as claimed. The accompanying drawings, which are incorporatedin and constitute a part of the specification, illustrate embodiments ofthe invention and together with the general description, serve toexplain the principles of the invention.

One or more cascade recognition systems for acquiring and following asubject with an unmanned aerial vehicle (UAV) are disclosed. In oneembodiment, the system includes one or more cameras fixed to the UAV andconfigured to capture one or more images. In one embodiment, the systemincludes an attitude control system configured to adjust one or morerotor speeds of the UAV. In one embodiment, the cascade recognitionsystem includes a visual recognition module configured to: isolate oneor more current image elements by analyzing the one or more imagescaptured; determine whether the one or more current image elements is apositive image element corresponding to the subject or a negative imageelement not corresponding to the subject; store the one or more positiveimage elements in a positive database; and store the one or morenegative image elements in a negative database. In one embodiment, thecascade recognition system includes a subject tracking module coupled tothe visual recognition module and to the attitude control system of theUAV and configured to: define a subject by capturing one or morereference images via at least one camera of the UAV; determine at leastone attribute of the subject by performing a comparison of at least onecurrent image element to a positive image element, a negative imageelement, or a reference image element; and direct the attitude controlsystem to adjust a rotor speed of the UAV based on the at least oneattribute of the subject.

One or more unmanned aerial vehicles (UAV) for acquiring and following asubject, are disclosed. In one embodiment, the UAV includes an attitudecontrol system configured to adjust one or more rotor speeds of the UAV(thereby adjusting a velocity of the UAV, a heading of the UAV, or anorientation of the UAV). In one embodiment, the UAV includes one or morecameras configured to capture one or more images. In one embodiment, theUAV includes one or more data storage units. In one embodiment, the UAVincludes one or more processors coupled to the one or more cameras, oneor more data storage units, and to the attitude control system, the oneor more processors are configured to: isolate one or more current imageelements from the one or more captured images; designate one or moreimage elements as a positive image element corresponding to the subject,a negative image element not corresponding to the subject, or areference image element corresponding to the subject; compare one ormore current image elements to at least one of a positive image elementstored in a first database, a negative image element stored in anegative database, and a reference image element stored in a thirddatabase; determine one or more attributes of the subject based on atleast the comparisons; and direct the attitude control system to adjustat least one rotor speed of the UAV based on the one or more attributesof the subject.

One or more methods of acquiring and following a subject via an unmannedaerial vehicle (UAV) are disclosed. In one embodiment, the methodincludes defining a subject by capturing at least one reference imagevia at least one camera of the UAV. In one embodiment, the methodincludes extracting at least one current image element from the at leastone captured image. In one embodiment, the method includes determiningwhether the at least one current image element is a positive imageelement corresponding to the subject or a negative image element notcorresponding to the subject. In one embodiment, the method includesstoring the at least one positive image element in a positive database.In one embodiment, the method includes storing the at least one negativeimage element in a negative database. In one embodiment, the methodincludes determining at least one attribute of the subject by performinga comparison of the at least one current image element to at least oneof a positive image element, a negative image element, and a referenceimage element.

BRIEF DESCRIPTION OF THE FIGURES

Embodiments of the presently disclosed invention may be betterunderstood by those skilled in the art by reference to the accompanyingfigures in which:

FIG. 1A illustrates a schematic diagram of a system for acquiring andfollowing a subject with an unmanned aerial vehicle (UAV), according toembodiments of this disclosure;

FIG. 1B illustrates a schematic diagram of a system for acquiring andfollowing a subject with one or more UAVs using multiple storagedatabases and a base station, according to embodiments of thisdisclosure;

FIG. 1C illustrates a schematic diagram of a UAV in a system foracquiring and following a subject, according to embodiments of thisdisclosure;

FIG. 2 is a process flow diagram of a method of acquiring and followinga subject with an UAV, according to embodiments of this disclosure;

FIGS. 3A-3B illustrate respective views of an image tracking/analysisprocess of the UAV utilizing macro-scaled visual recognition techniquesto acquire and center one or more images of a subject to track thesubject, according to embodiments of this disclosure;

FIGS. 4A-4B illustrate respective views of an image tracking/analysisprocess of an UAV utilizing medium-scaled visual recognition techniquesto acquire one or more images of a subject to track the subject,according to embodiments of this disclosure;

FIGS. 5A-5C illustrate respective views of an image tracking/analysisprocess of an UAV utilizing Haar-like features, Viola-Jones facialrecognition, and infrared/color pattern detection techniques to acquireone or more images of a subject to track the subject, according toembodiments of this disclosure; and

FIGS. 6A-6C illustrate respective side views of an UAV adjusting one ormore rotor speeds of the UAV due to one or more command gestures givenby a subject, according to embodiments of this disclosure.

DETAILED DESCRIPTION

Features of the invention in its various embodiments are exemplified bythe following descriptions with reference to the accompanying drawings,which describe the invention with further detail. These drawings depictonly selected embodiments of the invention, and should not be consideredto limit its scope in any way.

Embodiments of this disclosure generally relate to personal, visualrecognition and tracking and more particularly to a system, apparatus,and method for tracking with an Unmanned Aerial Vehicle (UAV). Inoperation, embodiments of this disclosure form an integrated systemutilizing a UAV, a camera, a visual recognition module, and a subjecttracking module, to select and follow a selected subject (ex.—targetedindividual). In this regard, the UAV captures a series of pictures ofmultiple subjects, any of which may be tracked. The series of picturesare separated into images with distinct image elements utilizingcascading visual recognition techniques and/or Haar-like features,Viola-Jones facial recognition and infrared, color pattern detection.The UAV stores the images and distinct image elements in one or moredatabases. A comparison is made between distinct image elements and oneor more stored/known image elements. Based on the comparison the UAV maybe directed to alter its position or heading to follow an identifiedsubject (e.g., keep the subject centrally framed).

Referring generally to FIG. 1A, a cascade recognition system 100 foracquiring and following a subject with an unmanned aerial vehicle (UAV)comprises the UAV 102 and one or more cameras 106. Applicant notes thatfor purposes of simplicity the camera 106, the UAV 102, and the othercomponents of the system 100 have been generally depicted in asimplified schematic diagram. This depiction, including the componentsand geometrical configuration, is not limiting and is provided forillustrative purposes only. Applicant further notes that the UAV 102,the camera 106, and the other components of the system 100 may includeany number of elements to carry out the image capturing and trackingprocesses contemplated in this disclosure. For example, the camera 106may include, but is not limited to, one or more optics, one or moreapertures, one or more image sensors, and one or more adjustable mounts.By way of another example, the UAV 102 may include, but is not limitedto, a different rotor configuration (e.g., three, six, or eight rotorsinstead of the four depicted in FIGS. 6A-6C), an audio tracking system,and one or more processors.

In one embodiment, the system 100 includes one or more processors (notshown). In one embodiment, the one or more processors are configured toexecute and implement one or more instructions or sets of instructions.In one embodiment, the one or more instructions executed and implementedare stored in one or more memories (e.g., non-transitory memory) (notshown).

In one embodiment, referring generally to FIG. 1A, the one or morememories are separated into one or more databases. For example, the oneor more memories may be separated into a positive database 136 forstoring one or more positive image elements associated with a firstsubject to be tracked, a negative database 138 a for storing one or morenegative image elements that are not associated with a subject to betracked, and a third database 138 b for storing one or more referenceimage elements.

In one embodiment, the one or more memories are separated according totheir content. For example, the one or more memories may be separatedinto a positive database 136 for storing one or more image elementsassociated with a subject to be tracked, and a negative database 138 afor storing one or more image elements that are not associated with thesubject being tracked. Applicant notes that the general configurationand geometrical orientation of the one or more memories or the one ormore databases is not limiting. Applicant further notes that otherconfigurations will be apparent to those skilled in the art.

In one embodiment, the system 100 includes one or more cameras 106 fixedto the UAV 102 and operably/communicatively coupled to the one or moreprocessors. In one embodiment, the one or more cameras 106 areconfigured to capture one or more images for visual recognition andanalysis processes executed by the one or more processors. In oneembodiment the images captured correspond to one or more subjects to betracked. In one embodiment, the one or more subjects to be tracked(e.g., subject 122) is a human, such as a skier. In this regard, thesubject is referred to as a subject (e.g., subject 122).

In one embodiment, referring to FIG. 1A, the one or more instructionsexecuted and implemented by the one or more processors of system 100 areseparated into multiple processes or sets of instructions, and furtherseparated into multiple sub-process or sub-sets of instructions. Forexample, the one or more instructions when executed and implemented bythe one or more processors may cause the one or more processors to:execute the capturing of one or more images using camera 106; execute animage compression and storage process 110; execute an image analysisprocess and a subject acquisition sub-process; execute a subject profilematching process, an image element classification sub-process, and asubject and environment distinguishing sub-process; execute a subjecttracking process 124; determine an attitude of the UAV and anorientation (e.g., direction of travel 128) of the subject 122; andadjust the attitude control system 126 such that one or more of theorientation and the direction of travel 132 of the UAV matches theorientation or direction of travel 128 of the subject 122.

In one embodiment, the cascade recognition system 100 includes a visualrecognition module 108. In one embodiment, the visual recognition module108 is operably/communicatively coupled to one or more cameras 106. Inone embodiment, the visual recognition module 108 further comprises theimage analysis process, the subject acquisition sub-process, the subjectprofile matching process, and the image element classificationsub-process. For example, referring to FIG. 1A, the visual recognitionmodule 108 may be configured to: isolate one or more current imageelements of a subject by analyzing the one or more images; determine 140whether the one or more current image elements is a positive currentimage element corresponding to a subject, or determine 142 whether theone or more current image elements is a negative current image elementnot corresponding to the subject; store the one or more positive currentimage elements in a positive database 136; and store the one or morenegative current image elements in a negative database 138 a.

In one embodiment, the cascade recognition system 100 includes a subjecttracking module 124 operably/communicatively coupled to the visualrecognition module 108 and to the attitude control system 126 of the UAV102. In one embodiment, such an operative/communicative couplingconfigures the UAV 102 to: define a subject (e.g., subject 122) bycapturing at least one reference image via at least one camera 106 ofthe UAV; designate one or more first images as a reference image, andthe associated image elements as reference image elements; determine 146at least one attribute of the subject by performing a comparison of atleast one current image element to at least one of a positive imageelement, a negative image element, and a reference image element; andadjust a rotor speed of at least one rotor of the UAV (e.g., direct theattitude control system 126 and coupled motor drivers 170/UAV rotors172) based on the at least one attribute of the subject.

In one embodiment, the visual recognition module 108 comprises a subjectacquisition module, or a subject acquisition process. In this regard,the visual recognition module 108 utilizes both macro-scaled andmicro-scaled visual recognition techniques/algorithms to determinewhether one or more reference image elements is a positive referenceimage element corresponding to the subject 122 or a negative referenceimage element not corresponding to the subject, populating negativedatabase 138 b accordingly.

In one embodiment, referring to FIG. 1B, the system 100 includes a basestation 150 configured to receive and process image data from multipleUAVs. For example, UAV 102, UAV 154, UAV 156, and UAV 158 may captureimages through onboard cameras and perform some image processingfunctions or associated subprocesses (e.g., at least one of: selecting asubject (e.g., potential subject 122); visual recognition 108; imagecompression and storage 110; and subject tracking 124). In this regard,each individual UAV may be further configured to transmit streamingimages, processed images, and/or image elements via wireless device 152to the base station 150.

In one embodiment, the base station 150 conducts the image processingfunctions and associated subprocesses for each UAV (e.g., at least oneof: selecting a subject (e.g., potential subject 122); visualrecognition 108; image compression and storage 110; and subject tracking124). The base station 150 may be further configured to communicate withthe individual attitude systems 126 of multiple UAVs, issuing commandsto one or more UAVs to acquire or track either a single subject 122 ormultiple subjects.

In one embodiment, referring to FIGS. 1A and 1C, the UAV 102 of system100 includes an airframe 104. In one embodiment, the system 100 includesa UAV with one or more rotors 172 with one or more motor drivers 170(ex.—electronic speed controls) fixed to the airframe 104, the one ormore rotors 172 configured to rotate at one or more rotor speeds. Forexample, the UAV 102 may be a rotorcraft incorporating any appropriatenumber of rotors (e.g., a tri-copter, quadcopter, hexacopter, oroctocopter).

In one embodiment, the UAV 102 of system 100 includes a power source.For example, the power source may be battery 160. In one embodiment, theUAV 102 includes a power controller 162. In one embodiment, the UAV 102includes one or more sensors for tracking the position of the UAV 102.For example, the one or more sensors may include attitude sensors 164.In one embodiment, the UAV includes one or more sensors for tracking theposition of a subject. For example, the one or more sensors may includeCMOS image sensor 166.

In one embodiment, the UAV 102 includes one or more cameras 106 operablyconnected to one or more processors, which are configured to moveaccording to one or more executed instructions or sets of executedinstructions. For example, the one or more cameras 106 may include oneor more camera mounts that move in multiple directions upon receivingone or more signals. In one embodiment, one or more components of theone or more cameras 106 are configured to move according to one or moreexecuted instructions or sets of executed instructions. For example, oneor more apertures may increase or decrease in size upon receiving one ormore signals.

In one embodiment, the one or more cameras 106 further comprise an imagestabilization module (e.g., set of programmed instructions to conductimage stabilization); one or more memory controllers; a high-speedmemory (e.g., partitioned into separate databases); a graphics engine;one or more outputs, including, but not limited to, a display output andan audio output; an image/video DSP; one or more devices configured toexecute wireless transmission, including, but not limited to, anEthernet device; and one or more devices for receiving and executinggeneral purpose input and output (I/O) commands. In one embodiment, theone or more cameras 106 may be configured to receive and/or communicatewith one or more external memory devices. For example, the one or morecameras 106 may include a slot for receiving and/or communicating withan external SDXC card 174 or other external flash memory devices.

In one embodiment, the high-speed memory includes one or more types ofmemory 168. For example, the one or more types of memory may include oneor more solid state drives (SSD) (e.g., one or more stacked NAND flashchips), and one or more random access memories (RAM) (e.g., DDR3 SDRAM).

In one embodiment, the UAV 102 of system 100 includes a wirelesstransceiver for sending and receiving one or more wireless signals. Forexample, UAV 102 may include Wi-Fi transceiver 152.

The embodiments of system 100 and UAV 102 (e.g., illustrated in FIGS.1A-1C) may be further configured as described herein. In addition,system 100 and UAV 102 may be configured to perform any other step(s) ofany of the method embodiment(s) described herein. The following methodembodiments relate to visual recognition and subject tracking using oneor more UAVs. It is generally recognized that system 100 and UAV 102 aresuitable for implementing the visual recognition and subject trackingsteps of the following embodiments. It is noted, however, the methodsdescribed below are not limited to the architectures of system 100 orUAV 102.

FIG. 2 illustrates a flow diagram depicting a method 200 of acquiringand following a subject with an unmanned aerial vehicle (UAV), inaccordance with one or more embodiments of the present disclosure.

In one embodiment, the cascade recognition system 100 conducts apreparatory step of generating one or more subject profiles, eachprofile corresponding to a subject 122. In one embodiment, thepreparatory step comprises gathering characteristic information for eachof a group of potential subjects. In this regard, the characteristicinformation will be used by the visual recognition module 108 toidentify positive image elements corresponding to the subject 122. Forexample, one or more cameras, including, but not limited to camera 106,may be used to create one or more subject profiles, which may includeone or more subject characteristic image elements specific to each of agroup of potential subjects (e.g., including subject 122) and extractedfrom captured images featuring the potential subjects. Camera 106 maycapture one or more images featuring a subject or potential subject,which the subject tracking module may designate as reference images.Reference image elements may then be isolated from the reference images.Reference image elements may be reference image elements correspondingto a subject or potential subject (e.g., a face or facial feature, aHaar-like feature, a hue or color scheme, a pattern or texture, a shapecorresponding to a body or body part of the subject) or reference imageelements not directly corresponding to the subject (e.g., the backgroundand environment in which the subject is pictured). In this regard, theone or more subject characteristic image elements may be stored in oneor more databases (e.g., in negative database 138 b) for a comparisonwith one or more real-time image elements (e.g., positive or negativeimage elements extracted from images captured in real time or near realtime).

In one embodiment, the preparatory step of system 100 also includesgathering non-subject characteristic information for a group ofreference objects. For example, one or more natural features, manmadestructures, or landmarks (e.g., tree 408, tower 412, or mountain 414,FIGS. 4A-4B) may exist along or in the path of a ski course. In thisregard, one or more cameras including, but not limited to, camera 106,may be used to create one or more subject profiles, which may includeone or more reference object characteristic image elements specific to agroup of potential subjects (e.g., tree 408, tower 412, or mountain414). In one embodiment, the one or more reference object characteristicimage elements are stored in one or more databases (e.g., as referenceimages in negative database 138 b) for a comparison with one or morereal-time image elements (e.g., negative image elements extracted fromimages captured in real time or near real time). In one embodiment, thesubject tracking module 124 further analyzes subject characteristic andsubject noncharacteristic reference image elements to determinereference relationships (e.g., a relative height of the subject comparedto a natural feature, a hue associated with the subject compared to thehue of a natural feature such as the sky, the absolute location of abackground element identified as a known natural feature or landmark).In one embodiment, reference relationships are stored in the one or moredatabases (e.g., negative database 138 b) for comparison with currentimage elements.

In one embodiment, the UAV 102 of system 100 also includes one or moretransmitters for transmitting one or more images. In one embodiment theUAV 102 of system 100 includes one or more receivers for receiving dataassociated with one or more subjects (e.g., subject 122) or one or morecommand gestures. In one embodiment, the UAV of system 100 includes oneor more transceivers (e.g., Wi-Fi transceiver 152) capable oftransmitting and receiving data.

In some embodiments, a user may receive image data captured by theonboard camera of a UAV, or processed by the cascade recognition system100, through a pair of goggles 182 or a similar linked device 180enabling remote viewing. Such a remote viewing system may additionallyallow the user to select (e.g., via a user interface of the smartphone180 or similar handheld device) an image element or a subject to acquireor track, or receive information associated with the position of the UAVor a subject. The remote viewing system may also incorporate an imageintensifier or similar device to assist in low-light image recognition(ex.—night vision).

In step 210, the method of acquiring and following a subject with an UAVincludes defining a subject by capturing at least one reference imagevia one or more cameras of the UAV. In one embodiment, the subject isdefined by a remote user, communicatively coupled to the system 100(e.g., via Wi-Fi), who selects a subject to follow or track. Forexample, a particular person or persons may be designated by a remoteuser as a subject 122. For instance, referring to FIG. 1B, a remote userusing a smartphone 180, interactive goggles 182, tablet, or other mediadevice may indicate a particular competitor or competitors they wish tofollow.

In one embodiment, the UAV may remotely or autonomously designate aperson as a subject for the UAV to track or follow in motion. Forexample, at a cross-country ski race the UAV may be assigned (e.g., by aremote user, by a base station, or by an operator/programmer of the UAV)to track a particular skier 122 along the course and relay informationabout his or her progress (e.g., still images, video feed, position,etc.) to coaches or viewers stationed elsewhere. In one embodiment, theUAV defines as its subject 122 an individual with various distinguishingfeatures, including, but not limited to, features such as height andweight, hair color and texture, facial composition, clothing color andtexture, facial hair or the lack thereof.

In one embodiment, the UAV 102 switches subjects between various membersof a team. In one embodiment, the UAV 102 tracks the lead skier 122whoever he or she may be. For example, the UAV 102 may be configured toselect one subject 122 from a finite set of potential subjects for whichassociated subject profiles have been uploaded, swapping the appropriateimage elements between one or more databases as a new subject isselected. In this regard, if multiple persons (subjects, potentialsubjects, and non-subjects) or potential subjects are in the field ofview of the UAV 102, then the UAV 102 may use cascade recognitiontechniques to distinguish subjects from non-subjects on a continualbasis.

In step 220, the UAV 102 captures one or more first images via the oneor more cameras 106. In one embodiment, capturing one or more firstimages includes utilizing one or more data compression algorithms 110 toefficiently transmit, store, and/or analyze the one or more imagescaptured.

In one embodiment, the one or more instructions executed by the one ormore processors of system 100 comprise an image compression and storageprocess 110. In one embodiment, the system 100 may initiate one or morealgorithms to compress the one or more images being captured by the oneor more cameras 106. For example, the one or more algorithms initiatedby system 100 may assign a gray-scale value to each pixel of the one ormore images captured. In this regard, the one or more algorithmsinitiated may recognize a series of repeating gray-scale values andassign to a portion of the one or more images captured at least one of acompression label, a compression position/coordinate, or a compressiongray-scale-repeat number (e.g., indicating the number of repeatingpixels associated with the compression value). In one embodiment, thesystem 100 or a base station (not shown) includes a codebook fordecompressing the one or more images using one or more of thecompression label, the compression position/coordinate, or thecompression gray-scale repeat number. Applicant notes that thealgorithms for image compression initiated by the imagecompression/storage module 110 may vary according to image compressionprocesses known in the art. In this regard, the examples for thecompression algorithms given herein are merely for illustrative andexplanatory purposes, and are not limiting.

In one embodiment, referring to FIGS. 1A-1B, a UAV (e.g., UAV 102)includes an onboard cascade visual recognition system 100 according tothe present disclosure, the system operably/communicatively coupled tothe one or more cameras 106. The one or more cameras 106 are configuredto capture a stream of images, either as a video feed or as periodicstill images. In one embodiment, a user may preprogram or adjust therate at which images are captured, increasing the capture rate forgreater precision or reducing the capture rate for greater efficiency ofoperation or longer battery life. For example, captured images mayinclude real-time (or near real-time) images of human beings who may ormay not be designated as subjects (e.g., defined in step 210 as subject122). In one embodiment, referring to FIG. 4A-4B, the captured imagesinclude additional background elements capable of conveying information,such as man-made structures or landmarks (buildings/towers) and naturalfeatures (terrain, vegetation, mountains, bodies of water). For example,the information conveyed by background (negative) image elements mayestablish reference relationships between one or more reference imageelements. In this regard, the information conveyed includes, but is notlimited to, time of day, weather, and medium-scale positioning. Forexample, an additional background image element may include the sky 410.Reference relationships involving the sky 410 may indicate time of day(darker but non-black sky may indicate evening or early morning),approximate position (the location of the sun may indicate a westerly oreasterly direction, or stellar patterns may indicate an approximatelatitude), or weather conditions (cloud cover or overcast skies mayindicate imminent precipitation).

In one embodiment of step 220, capturing one or more positive imageelements includes utilizing macro-scaled visual recognitiontechniques/algorithms to distinguish subjects (and their correspondingimage elements) from non-subjects (and their corresponding imageelements). For example, the macro-scaled techniques/algorithms include,but are not limited to, motion sensing capabilities, color and shapediscernment, and other broad-level visual recognitiontechniques/algorithms. For example, while tracking a subject 304 atspeed (e.g., at faster velocities while following the subject frombehind, as depicted in FIGS. 3A-3B), the visual recognition module 108may not be able to identify positive image elements corresponding to thesubject 304 based on identification of facial features or othermicro-scale characteristics. Therefore, at speed the visual recognitionmodule 108 may instead verify the subject 304 (and correspondingpositive image elements) via less precise but more efficientmacro-scaled or medium-scaled techniques. For example, the visualrecognition module 108 may identify the subject 304 based on a known hueor color scheme corresponding to the subject's clothing, based onwhether the general shape of the subject corresponds to reference imagesof the subject in motion (e.g., in various positions associated withskiing), or based on identification of a specific pattern (e.g., a QRcode) affixed to the clothing of the subject 122.

In step 230, the visual recognition module 108 extracts one or morecurrent image elements from the one or more first images (e.g.,real-time images) captured in step 220. In one embodiment, extractingthe one or more current image elements includes analyzing the one ormore real-time images to determine whether the current image elementsare positive image elements or negative image elements.

In one embodiment, referring to FIGS. 1A and 4A, the cascade recognitionsystem 100 of the UAV 102 may initially separate a captured andcompressed image 400 into a group of individual elements (402, 404, 406,408, 410, 412, 414) using medium-scale visual recognition techniquesand/or algorithms. For example, the visual recognition module 108 of UAV102 may identify one or more current image elements, including threehuman beings 402, 404, and 406, a tree in the foreground 408, the sky410, a tower 412, and a mountain 414 in the background, then extract theone or more current image elements for analysis and storage.

In one embodiment, the medium-scale visual recognition techniques and/oralgorithms include identifying, analyzing, and determining medium-scaledcharacteristics. For example, in one embodiment, medium-scaledcharacteristics may include shape, texture, color schemes, andcomponents of an object (e.g., distinguishing human arms/legs fromarboreal trunks/branches).

In one embodiment, referring to FIGS. 1A and 3A-4B, system 100 utilizesa combination of macro-scaled techniques/algorithms and medium-scaledtechniques/algorithms of the visual recognition module 108 to identifyand extract positive current image elements corresponding to one or moresubjects 122. For example, the system 100 may use camera 106 to makebroad scans to identify a group of image elements corresponding tosubjects, potential subjects, or non-subjects. When capturing images instep 220, the visual recognition module 108 may separate images (e.g.,image 400) into small chunks (image elements 402, 404, 406, 408, 410,412, and 414), divide them into sortable objects (e.g., subjects,non-subjects, and/or landmarks), and analyze (e.g., 140, 142, or 144)the specifics of the resulting image elements.

In step 240, cascade recognition system 100 determines whether the oneor more current image elements are positive image elements or negativeimage elements. In this regard, the visual recognition module 108determines 140 whether the one or more current image elements are one ormore positive image elements corresponding to the subject, or determines142 whether the one or more current image elements are negative imageelements not corresponding to the subject.

In one embodiment of step 240, referring generally to FIGS. 1A and5A-5C, the cascade recognition system 100 of UAV 102 further identifiesand extracts one or more image elements corresponding to specific humanbeings (e.g., subject persons 122 vs. non-subject persons) utilizingmicro-scaled visual recognition techniques/algorithms. In oneembodiment, the micro-scaled visual recognition techniques/algorithmsinclude, but are not limited to, using Haar-like features, Viola-Jonesfacial recognition, and infrared/color pattern detection techniques. Inone embodiment, micro-scaled visual recognition techniques are moreefficiently employed when assembling subject profiles associated withone or more subjects 122 in advance, while medium-scale or macro-scaletechniques are more efficiently employed for real-time or near real-timeimage analysis. For example, the UAV 102 may be able to acquiremultiple, detailed reference images of the subjects 122 from multipledistances and perspectives, assembling an individual profile for eachsubject 122 based on a detailed analysis of the subject's facialfeatures as in FIGS. 5A-5C. Once the subject's facial features have beenanalyzed, and the associated reference image elements added to thesubject profile and stored in database 138 b, the cascade recognitionsystem 100 may augment these micro-scale reference image elements withmedium-scale and macro-scale information, e.g., about the clothing ordimensions of each individual subject. The visual recognition module 108may then use medium-scale or macro-scale data to differentiate positiveimage elements from negative image elements if micro-scale visualrecognition techniques cannot be employed with sufficient confidence.

In one embodiment, the determination (e.g., 140, 142, or 144) is madeusing micro-scaled visual recognition techniques/algorithms including,but not limited to, using Haar-like features, Viola-Jones facialrecognition, and infrared/color pattern detection techniques. In thisregard, the one or more processors of UAV 102 utilize the identified andextracted positive image elements of step 230 to determine whether thepositive image elements correspond to the subject defined in step 210.The determination of negative image elements (e.g., elements notcorresponding to a subject) also occurs at this step. For example,referring to FIGS. 5A-5C, visual recognition module 108 may haveidentified an image element (e.g., 402, 404, or 406) as corresponding toa human being. In this regard, the visual recognition module 108 mayfurther analyze the element (e.g., 402) on a micro-scale to identify andextract the facial region 502 or individual facial features 504, 506,508, or 510 within region 502, which may or may not correspond to thesubject (e.g., subject defined in step 210).

Applicant notes the horizontal, medium-scaled facial feature recognitionalgorithms and/or Haar-like features depicted in FIGS. 5A and 5B aremerely for illustrative and explanatory purposes, as other algorithmsare encompassed by the visual recognition methods of this disclosure.For example, referring to FIG. 5C, a vertical, medium-scaled facialfeature recognition algorithm may be used to determine one or morefeatures of a specific human being.

In step 250, cascade recognition system 100 stores in a positivedatabase 136 the current positive image elements determined in step 240to correspond to the subject 122. For example, the visual recognitionmodule 108 may determine in step 240 that one or more current imageelements correspond to a subject (e.g., subject 122 or 304—defined instep 210). In this regard, the one or more current image elements aredetermined to be positive image elements and are stored in the positivedatabase 136, indicating a correspondence to the current subject 122.

In step 260, cascade recognition system 100 stores in a negativedatabase 138 a the current image elements determined in step 240 not tocorrespond to the subject. For example, the visual recognition module108 may determine in step 240 that one or more current image elements donot correspond to the subject (e.g., they correspond to non-subjects:tree 306, tree 408, tower 412, mountain 414; or they correspond to other(ex.—potential) persons who are not the defined subject, such assubjects 402, 404, and 406). In one embodiment, the positive database136 and negative database 138 a are populated with image elements thatestablish foreground and background position, hue and color scheme,patterns, or other distinguishing features to precisely determine whataspects of the current image correspond (or do not correspond) to thesubject 122.

In step 270, the subject tracking module 124 determines one or moreattributes of a subject 122 by performing a comparison of one or moreimage elements. In this regard, the comparison may be between the one ormore current image elements and a positive image element, a negativeimage element, or a reference image element. For example, the subjecttracking module 124 may compare a current positive image element (i.e.,extracted from the most recent image captured by camera 106 and analyzedby visual recognition module 108) corresponding to the subject 122 withone or more prior positive image elements stored in positive database136 and corresponding to earlier representations of the subject 122.Referring to FIG. 3A, the subject tracking module may compare currentand prior positive image elements corresponding to skier 304 (a selectedsubject currently being tracked by UAV 102) and determine that (e.g.,over the course of multiple frames) that skier 304 has moved slightly tothe left relative to his/her position in the frame 302 a. Accordingly,the subject tracking module 124 may direct the attitude control system126 of UAV 102 to keep skier 304 centrally framed by rotating UAV 102slightly to the left (resulting in frame 302 b). Referring to FIG. 3B,the subject tracking module 124 may instead determine that skier 304,while still centrally framed, is transitioning to a crouching position.In this instance, the subject tracking module 124 may instead instructthe attitude control system to adjust the pitch angle of UAV 102downward in anticipation of an approaching down-sloping portion of thecourse.

In one embodiment, referring to FIGS. 4A and 4B, at step 270 the subjecttracking module 124 may instead compare a current negative image elementwith a prior negative image element stored in negative database 138 a,or a second reference image element stored in negative database 138 b,to determine one or more attributes of subject 124 by derivinginformation about the environment through which subject 122 is passing.For example, the visual recognition module 108 may have previouslydetermined that tree 408, tower 412, and mountain 414 do not correspondto the subject, or to a human being, and stored any image elementsassociated with these objects in negative database 138 a (or asreference image elements in negative database 138 b). As additionalimage elements corresponding to tree 408, tower 412, or mountain 414accumulate in negative database 138 a, however, the subject trackingmodule 128 may compare sequential image elements to determine to whatextent, if any, the subject 122 is approaching each of tree 408, tower412, and mountain 414. This way the subject tracking module 124 mayestimate a relative distance between the subject 122 and tree 408, tower412, and mountain 414, or estimate a position of the subject 122relative to tree 408, tower 412, or mountain 414, depending on theavailable information.

In one embodiment, the subject tracking module 124 may compare currentor prior negative image elements with reference image elements stored innegative database 138 b to determine one or more attributes of subject122. For example, tower 412 may be a known landmark of known height andlocation. The subject tracking module may be tracking subject 122 at apredetermined distance, and may have estimated the height of subject 122from reference image elements in the subject profile of subject 122.Therefore if the current image displays a positive image elementcorresponding to subject 122 and a negative image element correspondingto tower 412 (the negative image element indicating a height of tower412 relative both to subject 122 and to the frame), the negative imageelement can be compared to a reference image element of tower 412 ifsuch a reference image element is stored in negative database 138 b. Ifa reference image element corresponding to tower 412 exists in negativedatabase 138 b, the reference image element may be associated withmetadata associated with a reference relationship between one or morereference image elements, e.g., an actual height or other dimension oftower 412, or a relative height of tower 412 as seen by a subject fromone or more distances or perspectives. In one embodiment, metadataassociated with a reference image element is associated with a referencerelationship between one or more reference image elements, e.g., Thesubject tracking module 124 may then compare one or more negative imageelements corresponding to tower 412 to the reference image element innegative database 138 b, thus making inferences about the relativedistance between subject 122 and tower 412 or a position of subject 122relative to tower 412.

In one embodiment, successive captured images may thus allow the subjecttracking module 124 to determine a heading of the subject (e.g., subject122) based on changes in relative position to fixed landmarks such astower 412. In one embodiment, the UAV 102 incorporates a GPS receiver orother position sensor connected to the cascade recognition system 100and capable of determining an absolute position or absolute heading ofthe subject 122.

In one embodiment, referring to FIGS. 1A and 6A-6C, the cascaderecognition system 100 determines one or more attributes of the subjectincluding one or more command gestures. In one embodiment, the cascaderecognition system 100 records one or more command gestures, eachcommand gesture associated with one or more command functions (e.g.,increase distance, change position, or abandon subject). For example,UAV 102 may be communicatively coupled with one or more cameras,including, but not limited to, camera 106. In this regard, the subject122 may direct the UAV 102 to record a sequence of images depicting theperformance of distinctive gestures. Referring to FIG. 6A, a distinctivecommand gesture may include a subject 122 briefly raising the left armat a 90° angle to the torso. A sequence of reference image elementscorresponding to this sequence of images (and thus to the commandgesture) may then be stored in the negative database 138 b. The sequenceof images or image elements may be further associated with a particularconfidence level and/or a counter.

In one embodiment, a UAV (e.g., UAV 102) tracking a subject (e.g.,subject 122), having identified at least one positive image elementcorresponding to the subject via the visual recognition module 108,compares the at least one positive image element to one or morereference image elements corresponding to a command gesture and storedin negative database 138 b via the subject tracking system 124. Forexample, a current positive image element may correspond to one or morereference image elements associated with a particular command gesture(and related command function, e.g., “reduce tracking distance by half”)to a predetermined confidence level. The subject tracking system 124 maythen increment a counter associated with the command gesture andcontinue to compare incoming positive image elements to the referenceimage element/s associated with the particular command gesture. Shouldthe counter reach a predetermined threshold (i.e., a predeterminednumber of sequential positive image elements correspond to the commandgesture), the cascade recognition system 100 may determine that theassociated command gesture has been identified to a sufficientconfidence level and direct the attitude control system 126 to executeany procedures or functions associated with the command gesture (i.e.,close to half the current tracking distance). In one embodiment, thesubject tracking module 124 will only initiate the command gesturedetection process if the gesture is performed by an identified subject122, ignoring any gestures performed by non-subjects.

In step 280, the attitude control system 126 adjusts at least one atleast one rotor speed of the UAV 102 based on the one or more determinedattributes of the subject (e.g., subject 122), as directed by thesubject tracking module 124. For example, referring to FIGS. 1A-1B,having determined one or more attributes of subject 122, which mayinclude at least an orientation (e.g., direction of travel 128 in FIG.1B), the subject tracking module 124 directs the attitude system 126 ofUAV 102 to adjust at least one rotor speed (thereby adjusting theposition, orientation, velocity, and/or direction of the UAV 102) tomatch at least the orientation or direction of travel 132 of the UAV 102to the orientation or direction of travel 128 of the subject 122.

In one embodiment the attitude control system 126 adjusts at least onerotor speed of the UAV 102 based on one or more determined commandgestures (e.g., attributes) of subject 122. For example, the visualrecognition module 108 may identify and extract image elements from acurrent image and determine those identified image elements correspondto the subject 122. THe subject tracking module 124 may then determinethat one or more positive image elements correspond to one or moredistinctive command gestures. The subject tracking module 124 may thendirect the attitude control system 126 of UAV 102 to perform anyprocedures or functions associated with the command gestures. In oneembodiment, referring to FIG. 1B, the subject tracking station 124 ofbase station 150 signals the attitude control systems 126 of(ex.—transmits attitude signals to) one or more UAVs 102, 154, 156, 158to execute procedures or functions associated with identified commandgestures.

In one embodiment, the one or more attitude signals cause UAV 102 toadjust one or more rotor speeds, such that the UAV tracks the subject122 from a different or new position. For example, referring to FIG. 6B,if a command gesture includes subject 122 holding the left arm at a 90°angle to the torso and the left forearm at a 90° angle to the leftbicep, the UAV 102 may then assume a new position more distant from thesubject 122.

Applicant notes that the command gestures given above are merely forillustrative and explanatory purposes. In one embodiment, the UAV 102,camera 106, visual recognition module 108, and subject tracking module124 are further configured to recognize multiple distinctive commandgestures, each of which may stop or adjust a one or more rotor speeds ofthe UAV 102. For example, referring to FIG. 6C, a distinctive commandgesture of the left arm held out straight away from the torso may beassociated with stopping the tracking of the subject 122. By way ofanother example, a distinctive command gesture of the right arm beingheld out straight away from the torso may be associated with changing atracking angle of the UAV 102. Applicant notes that additionaldistinctive command gestures associated with additional commandprocedures or functions will be recognized by those skilled in the art,and are encompassed in embodiments of this disclosure.

Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “connected”, or “coupled”, toeach other to achieve the desired functionality, and any two componentscapable of being so associated can also be viewed as being “couplable”,to each other to achieve the desired functionality. Specific examples ofcouplable include but are not limited to physically mateable and/orphysically interacting components and/or wirelessly interactable and/orwirelessly interacting components and/or logically interacting and/orlogically interactable components.

While particular aspects of the subject matter described herein havebeen shown and described, it will be apparent to those skilled in theart that, based upon the teachings herein, changes and modifications maybe made without departing from the subject matter described herein andits broader aspects and, therefore, the appended claims are to encompasswithin their scope all such changes and modifications as are within thetrue spirit and scope of the subject matter described herein.

We claim:
 1. A system for acquiring and following a subject with atleast one unmanned aerial vehicle (UAV) the UAV having at least onerotor configured to rotate at a rotor speed, the system comprising: atleast one camera fixed to the UAV, the at least one camera configured tocapture at least one image; at least one attitude control systemconfigured to adjust a rotor speed of the at least one rotor; at leastone data storage unit fixed to the UAV; a plurality of databases storedin the data storage unit, including at least a first database of atleast one first image element corresponding to the subject and a seconddatabase of at least one second image element not corresponding to thesubject; at least one visual recognition module coupled to the at leastone camera and to the plurality of databases, the at least one visualrecognition module configured to: extract at least one current imageelement from the at least one image; determine whether the at least onecurrent image element is a first current image element or a secondcurrent image element; store the at least one first current imageelement in the first database; and store the at least one second currentimage element in the second database; and at least one subject trackingmodule coupled to the at least one visual recognition module and to theat least one attitude control system, the at least one subject trackingmodule configured to: define the subject by analyzing at least onereference image captured by the at least one camera; determine at leastone attribute of the subject by performing at least one comparison of atleast one current image element with at least one of a first imageelement and a second image element; and direct the attitude controlsystem of the UAV to adjust at least one rotor speed of the UAV based onthe at least one attribute; and at least one transmitter configured totransmit the at least one image via wireless link.
 2. The system ofclaim 1, wherein the at least one subject tracking module is configuredto designate at least one image as a reference image; designate at leastone first current image element of the reference image as a firstreference image element; designate at least one second current imageelement of the reference image as a second reference image element;determine at least one reference relationship between at least two of afirst reference image element and a second reference image element;populate at least one of the first database and the third database withthe at least one first reference image element; populate at least one ofthe second database and the third database with the at least one secondreference image element; and populate the third database with the atleast one reference relationship.
 3. The system of claim 2, wherein theat least one subject tracking module is configured to determine at leastone attribute of the subject by performing at least one comparison of atleast one current image element with at least one of a first imageelement, a second image element, and a reference image element.
 4. Thesystem of claim 2, wherein the at least one reference relationshipcorresponds to at least one of a location, a dimension, an orientation,a time, an atmospheric condition, and a gesture of the subject.
 5. Thesystem of claim 4, wherein the at least one subject tracking module isconfigured to populate the third database with at least one procedureassociated with a gesture of the subject.
 6. The system of claim 5,wherein the at least one subject tracking module is configured to directthe at least one attitude control system of the UAV to adjust at leastone rotor speed of the at least one UAV based on the at least oneprocedure.
 7. The system of claim 1, wherein the at least one firstimage element corresponds to at least one of a hue of the subject, acolor scheme of the subject, a texture of the subject, a shape of thesubject, a face of the subject, a facial feature of the subject, and abody part of the subject.
 8. The system of claim 1, wherein the at leastone second image element corresponds to at least one of a non-subjectperson, a manmade structure, and a natural feature.
 9. The system ofclaim 1, wherein the at least one visual recognition module isconfigured to determine whether the at least one current image elementcorresponds to at least one of a foreground element, a backgroundelement, a hue, a color scheme, a texture, a face, a facial feature, aHaar-like feature, a body part, a shape, a manmade structure, a naturalfeature, and a landmark.
 10. The system of claim 1, wherein the at leastone visual recognition module is configured to determine whether the atleast one current image element corresponds to the subject via at leastone of a facial feature recognition algorithm, a facial recognitionalgorithm, a color pattern detection algorithm, and an infrareddetection algorithm.
 11. The system of claim 1, further comprising: twoor more UAVs, each UAV having an airframe; at least one rotor fixed tothe airframe, each rotor configured to rotate at a rotor speed; at leastone camera fixed to the UAV, the at least one camera configured tocapture at least one image; an attitude control system configured toadjust a rotor speed of the at least one rotor; and at least onetransmitter configured to transmit the at least one image via wirelesslink.
 12. The system of claim 11, wherein the data storage unit, thevisual recognition module, and the subject tracking module are embodiedin a ground-based station coupled to the at least one UAV via wirelesslink.
 13. The system of claim 11, wherein each UAV of the two or moreUAVs comprises: a data storage unit of the at least one data storageunit; a visual recognition module of the at least one visual recognitionmodule; and a subject tracking module of the at least one subjecttracking module.
 14. The system of claim 1, further comprising: at leastone display device wirelessly coupled to the at least one transmitter,the at least one display device configured to receive the at least oneimage and display the at least one image to a user.
 15. The system ofclaim 14, wherein the at least one display device includes at least oneof a pair of goggles configured to display the at least one image to thewearer, a smartphone, and a portable computing device.
 16. An unmannedaerial vehicle (UAV), comprising: an airframe; at least one rotor fixedto the airframe, each rotor configured to rotate at a rotor speed; atleast one camera fixed to the UAV, the at least one camera configured tocapture at least one image; an attitude control system configured toadjust a rotor speed of the at least one rotor; a data storage unitfixed to the UAV; a plurality of databases stored in the data storageunit, including at least a first database of at least one first imageelement corresponding to the subject and a second database of at leastone second image element not corresponding to the subject; a visualrecognition module coupled to the at least one camera and to theplurality of databases, the visual recognition module configured to:extract at least one current image element from the at least one image;determine whether the at least one current image element is a firstcurrent image element or a second current image element; store the atleast one first current image element in the first database; and storethe at least one second current image element in the second database;and a subject tracking module coupled to the visual recognition moduleand to the attitude control system, the subject tracking moduleconfigured to: define the subject by analyzing at least one referenceimage captured by the at least one camera; determine at least oneattribute of the subject by performing at least one comparison of atleast two of a first image element, a second image element, and acurrent image element; and direct the attitude control system of the UAVto adjust at least one rotor speed of the UAV based on the at least oneattribute; and at least one transmitter configured to transmit the atleast one image via wireless link.
 17. A method of acquiring andfollowing a subject via at least one unmanned aerial vehicle, (UAV),comprising: defining a subject by capturing at least one reference imagevia at least one camera of the at least one UAV; capturing at least onefirst image via the at least one camera; extracting at least one currentimage element from the at least one first image; determining whether theat least one current image element is a first image elementcorresponding to the subject or a second image element not correspondingto the subject; storing the at least one first image element in a firstdatabase; storing the at least one second image element in the seconddatabase; and a second database; determining at least one attribute ofthe subject by performing a comparison of the at least one current imageelement to at least one of a first image element, a second imageelement, and a reference image element; and adjusting at least one rotorspeed of the at least one UAV based on at least the at least oneattribute of the subject.
 18. The method of claim 17, wherein defining asubject by capturing at least one reference image via at least onecamera of the at least one UAV includes: capturing at least one imageincluding the subject; designating the at least one image as a referenceimage; extracting at least one reference image element from the at leastone reference image; determining whether the at least one referenceimage element is a first reference image element corresponding to thesubject or a second reference image element not corresponding to thesubject; determining at least one reference relationship between atleast two of a first reference image element and a second referenceimage element; populating at least one of the first database and a thirddatabase with the at least one first reference image element; populatingat least one of the second database and the third database with the atleast one second reference image element; and populating the thirddatabase with the at least one reference relationship.
 19. The method ofclaim 18, wherein the at least one reference relationship corresponds toat least one of a location, a dimension, an orientation, a time, and anatmospheric condition.
 20. The method of claim 18, wherein populatingthe third database with the at least one reference relationshipincludes: populating the third database with at least one procedureassociated with a gesture of the subject.
 21. The method of claim 20,wherein adjusting at least one rotor speed of the at least one UAV basedon at least the at least one attribute of the subject includes:adjusting at least one rotor speed of the at least one UAV based on theat least one procedure.
 22. The method of claim 17, wherein determiningwhether the at least one current image element is a first image elementcorresponding to the subject or a second image element not correspondingto the subject includes: comparing the at least one current imageelement to at least one of a first reference image element and a secondreference image element.
 23. The method of claim 17, wherein determiningwhether the at least one current image element is a first image elementcorresponding to the subject or a second image element not correspondingto the subject includes determining whether the at least one currentimage element corresponds to at least one of a foreground element, abackground element, a hue, a color scheme, a texture, a face, a facialfeature, a Haar-like feature, a body part, a shape, a manmade structure,a natural feature, and a landmark.
 24. The method of claim 17, whereindetermining whether the at least one current image element is a firstimage element corresponding to the subject or a second image element notcorresponding to the subject includes determining whether the at leastone current image element is a first image element corresponding to thesubject or a second image element not corresponding to the subject viaat least one of a facial feature recognition algorithm, a facialrecognition algorithm, a color pattern detection algorithm, and aninfrared detection algorithm.
 25. The method of claim 17, whereindetermining at least one attribute of the subject by performing acomparison of the at least one current image element to at least one ofa first image element, a second image element, and a reference imageelement includes: determining at least one of a location of the subject,a position of the subject, an orientation of the subject, a gesture ofthe subject, and a heading of the subject.
 26. The method of claim 17,further comprising: transmitting the at least one image to at least onenon-subject user via wireless link.